This document compares the results of at least 2 CASAL model configurations (base and at least one sensitivity) and up to 8 Casal2 model configurations (3 BetaDiff, 2 CppAD, and 3 ADOL-C).
The CASAL model sensitivity 1 has a smaller minimisation tolerance value than the CASAL base model (1e-6 vs. 2e-3).
The Casal2 ADOL-C and BetaDiff low tolerance models have a smaller tolerance value than the CASAL base model (1e-6 vs. 2e-3). The Casal2 CppAD models have a tolerance value of 1e-9.
The main characteristics of the Test Case HAK (hake) CASAL model are:
Observation data include:
Parameters estimated include:
The CASAL MCMC options include
The Casal2 ADOL-C and BetaDiff MCMC options include
The Casal2 CppAD MCMC runs use the BetaDiff covariance matrices since Casal2 does not output the covariance matrix from CppAD minimisation. The Casal2 CppAD MCMC options include
## [1] "Fri Jul 31 12:17:01 2020"
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_NZ.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_NZ.UTF-8 LC_COLLATE=en_NZ.UTF-8
## [5] LC_MONETARY=en_NZ.UTF-8 LC_MESSAGES=en_NZ.UTF-8
## [7] LC_PAPER=en_GB.UTF_8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_NZ.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] casal2_1.0 casal_2.30 devtools_2.3.1 usethis_1.6.1 ggthemes_4.2.0
## [6] gridExtra_2.3 coda_0.19-3 ggmcmc_1.4.1 ggplot2_3.3.2 tidyr_1.1.0
## [11] dplyr_0.8.5
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.0 xfun_0.16 remotes_2.2.0 purrr_0.3.4
## [5] lattice_0.20-38 colorspace_1.4-1 vctrs_0.3.2 testthat_2.3.2
## [9] htmltools_0.5.0 yaml_2.2.1 rlang_0.4.7 pkgbuild_1.1.0
## [13] pillar_1.4.6 glue_1.4.1 withr_2.2.0 RColorBrewer_1.1-2
## [17] sessioninfo_1.1.1 lifecycle_0.2.0 plyr_1.8.6 stringr_1.4.0
## [21] munsell_0.5.0 gtable_0.3.0 evaluate_0.14 memoise_1.1.0
## [25] knitr_1.29 GGally_1.4.0 callr_3.4.3 ps_1.3.3
## [29] fansi_0.4.1 Rcpp_1.0.5 scales_1.1.1 backports_1.1.8
## [33] desc_1.2.0 pkgload_1.1.0 fs_1.4.2 digest_0.6.25
## [37] stringi_1.4.6 processx_3.4.3 grid_3.6.0 rprojroot_1.3-2
## [41] cli_2.0.2 tools_3.6.0 magrittr_1.5 tibble_3.0.3
## [45] crayon_1.3.4 pkgconfig_2.0.3 ellipsis_0.3.1 prettyunits_1.1.1
## [49] assertthat_0.2.1 rmarkdown_2.3 reshape_0.8.8 R6_2.4.1
## [53] compiler_3.6.0
# source('../../R-functions/report_read_in_CASAL_MPD_files.R')
source('../../R-functions/report_read_in_CASAL_MCMC_files.R')
source('../../R-functions/report_read_in_Casal2_MPD_files.R')
source('../../R-functions/report_read_in_Casal2_MCMC_files.R')
For the diagnostics below, the last 10000 samples for each chain are used and subsampled at 10, so that 1000 samples are input into the diagnostic functions.